Artificial Intelligence in Healthcare – A Comprehensive Overview

We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for.

Natural language processing (NLP) seems to see less use in pharma than AI approaches such as machine vision and predictive analytics, but nevertheless there are a few applications for NLP in pharma. The industry deals mostly with structured data, but in some business areas, unstructured data is the norm. In this article, we discuss how natural language processing can help pharmaceutical companies make sense of their unstructured data and use it to make decisions.

It may feel as though AI applications like machine vision and natural language processing hold the most potential value to pharmaceutical companies because of their capabilities to intake and transform unstructured medical data. This is especially true with machine vision, as medical imaging data can be used across multiple departments when analyzed by AI software.

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